Electrical Load Forecasting Using a Hybrid Large Margin Nearest Neighbor Method

Abstract

Load forecasting is a key component of electric utility operations and planning. Because of today's highly developed electricity markets and rapidly growing power systems, load forecasting is becoming an essential part of power system operation scheduling. This paper proposes a new short-term load forecasting model based on the large margin nearest neighbor (LMNN) classification algorithm to improve prediction accuracy. The accuracy of many classification methods, such as k-nearest neighbor (k-NN), is significantly influenced by the technique used to calculate sample distances. The Mahalanobis distance is one of the most widely used methods for calculating distance. Numerous techniques have been used to enhance k-NN performance in recent years, including LMNN. Our proposed approach aims to solve the local optimum problem of LMNN, compute data similarities, and optimize the cost function that establishes the distances between instances. Before using gradient descent to determine the ideal parameter values for the cost function, we employ a genetic algorithm to shrink the size of the solution space. Additionally, our method's forecasting errors are contrasted with those of the BPNN and ARMA models. The comparative findings show how well the recommended forecasting model performs in short-term load forecasting.